JP7503945B2 - Information collection system and information collection method - Google Patents

Description

The present invention relates to an information collection system and an information collection method.

Traditionally, in factories and offices in the manufacturing industry, there are many tasks that make up the manufacturing process when manufacturing finished products. In each task that makes up the manufacturing process, on-site data is generated and collected when the task is completed or a specified event occurs, and this on-site data is analyzed to optimize the work efficiency of each task. On the other hand, when different departments manage each task, it becomes difficult to share information between departments, and improvements to problems that cross departments (tasks) are not progressing. One of the reasons why improvements to problems that cross departments (tasks) are not progressing is that each department uses different systems to manage their tasks, and the management system is not unified. In addition, each department analyzes the collected on-site data to optimize the work efficiency within the department, so if an overall optimization of a series of manufacturing processes is sought, the efficiency of one's own department may decrease, resulting in a lower evaluation of the department.

However, in the manufacturing industry, as we move from mass production to small-lot, high-mix production, collaboration between departments is essential to improve the production efficiency of the entire manufacturing process. The only way to promote collaboration between departments is to present problems using factual data (factual information) that can be used by all departments to analyze the problems in common. It is believed that collaboration between departments can be promoted by presenting the on-site data managed by one department in relation to the on-site data managed by other departments.

Patent Document 1 describes how the minimum amount of information required to associate work information and work-related information contained in on-site data can be stored and managed as association data, thereby reducing the capacity of the storage device that stores the association data, and how this association data can be used to facilitate the analysis of on-site data at each site.

JP 2019-153051 A

However, in Patent Document 1, it is not possible to track the execution history of work when multiple parts with the same management information are used. For example, when a defect is discovered in a group of parts with a certain lot number and a search is made for a finished product that uses parts with that lot number, tracking becomes extremely difficult if the lot number is mixed with parts with other lot numbers. Similarly, tracking becomes difficult when a branch occurs in which multiple parts with the same management number are used individually in different products.

The present invention aims to make it possible to track the execution history of tasks that use multiple parts to which the same management information is assigned.

In order to achieve the above-mentioned objective, one representative information collection system of the present invention is a system including a data storage device that accumulates on-site data that is set or generated each time multiple tasks included in a process are performed, and an information system that processes the on-site data accumulated in the data storage device, wherein the on-site data includes identification information of an input item accepted as a target for processing in the corresponding task and identification information of an output item output as a result of the processing in the task, and the information system determines the continuity of tasks using the identification information of the output item in the previous task and the identification information of the input item in the subsequent task, thereby making it possible to track the execution history of the multiple tasks for a specified item, and the information system replaces the identification information when a merging of input items occurs, which causes multiple input items that have been given different identification information to be grouped together in an indistinguishable manner, and/or a branching of output items occurs, which causes multiple output items to be separated and handled individually.
Furthermore, one representative information collection method of the present invention is an information collection method in a system including a data storage device that accumulates on-site data that is set or generated each time multiple tasks included in a process are performed, and an information system that processes the on-site data accumulated in the data storage device, and includes a step in which the data storage device accumulates the on-site data including identification information of an input item accepted as a target for processing in the corresponding task and identification information of an output item output as a result of the processing in the task, and a step in which the information system tracks the execution history of the multiple tasks for a specified item by determining the continuity of the tasks using the identification information of the output item in the previous task and the identification information of the input item in the subsequent task, and further includes a step in which the information system replaces the identification information when a merging of input items occurs, which causes multiple input items that have been given different identification information to be grouped together in an indistinguishable manner, and/or a branching of output items to separate multiple output items and handle them individually.

According to the present invention, it is possible to track the execution history of a task that uses multiple parts to which the same management information is assigned. Problems, configurations, and advantages other than those described above will become clear from the description of the embodiment below.

1 is a diagram for explaining the connection relationship between an information collecting and displaying system according to an embodiment, a data generating device, and each storage unit that stores actual data. FIG. 2 is a block diagram illustrating functions of the information collecting and displaying system. 10 is a diagram illustrating an example of a structure of definition information created by a relevance data model creating unit; FIG. FIG. 2 is a diagram illustrating an example of nodes connected based on connection information. FIG. 2 is a diagram illustrating an example of configuration information of on-site data. FIG. 2 is a schematic diagram illustrating only the connection relationships of each piece of information in the association data. FIG. 13 is a diagram illustrating an example of a data extraction tool displayed on a user interface. 13 is a flowchart of a process of extracting relational data in a relational data search unit. This is a processing procedure for tracking tasks in the order in which they are performed. This is a processing procedure for tracing back the execution order of tasks. FIG. 13 is an explanatory diagram of merging and branching of articles. 13 is a specific example of a setting screen relating to merging and branching. This is a processing procedure (including conversion of IDs related to merging and branching) when tracing in accordance with the execution sequence of tasks. This is a processing procedure for tracing back the execution order of tasks (including conversion of IDs related to merging and branching). FIG. 13 is a diagram illustrating an example of a data extraction tool when using a usage probability. 13 is a specific example of a merging range. This is a concrete example of the branch range. 13 is a flowchart showing a process for calculating a use probability. FIG. 11 is an explanatory diagram of cases where merging and branching occur. This is a specific example of the usage probability. 13 is a concrete example of the usage probability for a combination of a join and a branch. This is a processing procedure when tracing in the order in which tasks are performed (including processing related to the probability of merging and branching). This is a processing procedure for tracing back the execution order of tasks (including processing related to the probability of merging and branching).

The following describes an information collection and display system 1 according to an embodiment of the present invention with reference to the drawings. In this embodiment, the information collection and display system 1 is described as being applied to a system that collects and displays on-site data generated in each manufacturing process in a manufacturing plant.

[overall structure]
FIG. 1 is a diagram for explaining the connection relationship between an information collecting and displaying system 1, a data generating device 5, and storage units 3 and 4 for storing actual data.

As shown in FIG. 1, the information collection and display system 1 is connected to a network 2. To the network 2, a plurality of data generating devices 5a, 5b, 5c (hereinafter, when there is no particular distinction between the data generating devices 5a, 5b, 5c, they will be simply referred to as data generating devices 5) that collect or generate actual data, a master data storage unit 3, and a transaction data storage unit 4 that stores actual data (hereinafter, referred to as field data 100), are connected, and these data generating devices 5, master data storage unit 3, and transaction data storage unit 4 are connected to the information collection and display system 1 via the network 2.

The data generating device 5 is, for example, a barcode reader that obtains the work log of a worker, a PC or server that collects the work log (e.g., data generating device 5a), a machine that processes parts or assembles finished products (e.g., data generating device 5b), or a sensor that collects inspection information from RFIDs (Radio Frequency IDentifiers) attached to parts or finished products (e.g., data generating device 5c). The on-site data 100 (actual data) collected or generated by these data generating devices 5 is transmitted via the network 2 to the information collection and display system 1, the master data storage unit 3, or the transaction data storage unit 4.

The master data storage unit 3 is, for example, a storage device such as a server or memory, and stores a model that defines what information is to be stored in the transaction data storage unit 4, etc. This model is also called master data. In other words, by changing the master data (model) defined in the master data storage unit 3, it is possible to change what on-site data 100 (type of information to be collected) is to be collected from the data generating device 5. The master data in this master data storage unit 3 can be set or changed via an external device (not shown).

The transaction data storage unit 4 is, for example, a storage device such as a server or memory, and stores site data 100 including information defined by the master data in the master data storage unit 3. In an embodiment, the transaction data storage unit 4 stores site data 100 (see FIG. 5) such as identification information, occurrence date and time, and actual measured values.

[Information collection and display system]
Next, we will explain the main functions of the information collection display system 1. The information collection display system 1 has a central processing unit (CPU) that performs overall control of the information collection display system 1, a storage device (Read Only Memory: ROM) that stores each control program for controlling the information collection display system 1, a primary storage device (Random Access Memory: RAM) that temporarily stores information processed by the CPU, and a hard disk drive (HDD), and the following functions are realized by the CPU executing each control program stored in the ROM.
FIG. 2 is a block diagram illustrating the functions of the information collecting and displaying system.

As shown in FIG. 2, the information collection and display system 1 has a relationship data model creation unit 10, a relationship data registration unit 11, a relationship data search unit 12, an accumulated data acquisition unit 13, an analysis data accumulation unit 14, a relationship data accumulation unit 15, a data provision API unit 16, a comparison data definition unit 17, and a temporary accumulation unit 18.

As described above, the information collection and display system 1 is connected to the dictionary database 6, the transaction data storage unit 4, the data generating device 5, and the master data storage unit 3 via the network 2. Furthermore, the relevance data model creation unit 10 and the relevance data search unit 12 of the information collection and display system 1 are connected to a user interface 7 such as a display, and information resulting from processing by the information collection and display system 1 is displayed on the user interface 7 and provided to the user, or the user can input predetermined information to the relevance data model creation unit 10 or the relevance data search unit 12 via the user interface 7. Furthermore, in the information collection and display system 1, the analysis data storage unit 14 and the data provision API unit 16 are capable of providing information resulting from processing by the information collection and display system 1 to an application 8 external to the information collection and display system 1.

Based on the model data input from the user interface 7, the relevance data model creation unit 10 reads out master data corresponding to the model data from the master data storage unit 3 and creates definition information 300 (described below). The definition information 300 created by the relevance data model creation unit 10 has a predetermined data structure and defines the data structure in which the relevance data registration unit 11 acquires the site data 100 from the data generation device 5.

The relevance data registration unit 11 receives the site data 100 from the data generating device 5, and structures the site data 100 based on the data structure defined in the definition information 300 acquired from the relevance data model creation unit 10. The relevance data registration unit 11 then determines whether the site data 100 acquired according to the definition information 300 is business information, worker, machine, work procedure, or material (parts) based on the identification information assigned to the site data 100. Hereinafter, these may be referred to as 4M information (or 4M node) or business-related information, taking the initial letter M of the worker (Man), machine (Machine), work procedure (Method), and material (Material).

Here, the relational data registration unit 11 is connected to the comparison data definition unit 17. The relational data definition unit 17 defines the relationship between the 4M information determined by the relational data registration unit 11 and the identification information assigned to the site data 100, and the relational data registration unit 11 associates the 4M information determined by the relational data registration unit 11 with the actually acquired site data 100 based on this definition (maps the identification information of the site data 100 to the 4M information). The relational data registration unit 11 transmits the 4M information after the site data 100 is associated to the relational data storage unit 15. Furthermore, the relational data registration unit 11 is connected to the temporary storage unit 18. The temporary storage unit 18 is a storage device such as a memory, and information for configuring the connection relationship (generating a connection line) between each piece of information (business information and 4M information) included in the site data 100 is registered.

The stored data acquisition unit 13 acquires the field data 100 from the transaction data storage unit 4 and the master data storage unit 3 based on the relationship data 200 stored in the relationship data storage unit 15.

When a user selects 4M information (first information) for a specific task from the user interface 7, the relevance data search unit 12 searches for other 4M information (second information) for the task related to the 4M information (first information) based on the identification information assigned to the 4M information (first information), and presents the searched 4M information (second information) to the user by displaying it on the user interface 7 using hatching or color coding. The relevance data search unit 12 also transmits the connection relationship between the selected 4M information (first information) and the searched 4M information (second information) to the analysis data accumulation unit 14. An example of a method for searching 4M information by the relevance data search unit 12 will be described later.

The analytical data storage unit 14 stores the connection relationship between the selected 4M information (first information) sent from the relational data search unit 12 and the searched 4M information (second information). The analytical data storage unit 14 sends the connection relationship of this stored 4M information to the application 8, so that other 4M information of the business related to the selected 4M information can be used in various analytical methods.

The data providing API (Application Programming Interface) unit 16 transmits data associated by the association data 200 to the application 8. Here, the data providing API unit 16 is connected to the dictionary database 6. Here, the item names of the 4M information used in each manufacturing process (business) may differ for each manufacturing process. Therefore, different words with the same meaning for the item names of the 4M information used in each manufacturing process are registered in the dictionary database 6 in association with each other, and the data providing API unit 16 can accurately acquire data associated by the association data 200, including not only the same words but also different words with the same meaning.

Figure 3 is a diagram illustrating an example of the structure of definition information 300 created by the relevance data model creation unit 10.

As shown in FIG. 3, the definition information 300 is held by the aforementioned relationship data model creation unit 10, and is composed of task information 310 as the definition information for the task node 210, worker information 320 as the definition information for the worker node 230, part information 330 as the definition information for the part node 220, finished product information 340 as the definition information for the finished product node 250, machine information 350 as the definition information for the machine node 240, and work procedure information 360 as the definition information for the work procedure node 260.

Business information 310 includes business identification information 311, operating time information 312, operating data access information 313, and connection information 314.

The business identification information 311 is identification information that defines what type of business is involved in the business node 210; for example, in a vehicle manufacturing process, identification information is set that can uniquely identify chassis press processing work and assembly work. This business identification information 311 is identification information that specifies the relationship with other businesses, and has the function of relating multiple businesses. This business identification information 311 also includes key information for obtaining on-site data 100 (actual data) from the transaction data storage unit 4 or master data storage unit 3 based on the operational data access information 313.

Operating time information 312 is set with information relating to the start and end times when a specific task is performed. For example, when a specific task involves producing a finished product from parts, the time the parts are input is set as the start time, and the time the finished product is produced is set as the end time. This operating time information 312 is used as information for narrowing down the search from a time perspective when referring to operation information on workers, parts, machines, etc. By having this operating time information 312, definition information 300 can provide the user with time series data when the task was performed, even if the data is time series data that is difficult to associate from a perspective other than chronological order.

The operational data access information 313 is used when accessing the field data 100 stored in the transaction data storage unit 4, which is managed outside the information collection and display system 1. Specifically, the operational data access information 313 includes the memory addresses of the field data 100 stored in the transaction data storage unit 4 or the master data storage unit 3. The connection information 314 describes the From-To of the connection information for associating the aforementioned business node 210 with the 4M node. Details of the connection information 314 will be described later (see Figure 4).

The worker information 320 is composed of worker identification information 321 and operational data access information 322. The worker identification information 321 is set with an identifier for identifying the worker in charge when the task was performed in the worker node 230 described above. This worker identification information 321 is used as a key for accessing the worker's work records (logs) stored in the transaction data storage unit 4 or the master data storage unit 3 based on the operational data access information 322.

The part information 330 is composed of part identification information 331 and operation data access information 332. The part identification information 331 is defined to identify materials (parts) used in the business, and the operation data access information 332 includes memory addresses for accessing information such as the material and processing history of the parts stored in the transaction data storage unit 4 or the master data storage unit 3.

Finished product information 340 is composed of finished product identification information 341 and operational data access information 342. Finished product identification information 341 is defined to identify finished products generated in a business operation, and operational data access information 342 includes memory addresses for accessing information such as the processing history of finished products stored in the transaction data storage unit 4 or the master data storage unit 3.

Machine information 350 includes machine identification information 351 and operation data access information 352. Machine identification information 351 defines information that identifies a machine used in a business. For example, this machine identification information 351 makes it possible to identify whether a machine used in a business is a press machine, a coating machine, or an assembly machine. When a user wishes to refer to operation data when a business is being performed by a machine, the user can refer to the operation data by using operation data access information 352 and machine identification information 351.

Finally, the work procedure information 360 is composed of work procedure identification information 361 and work procedure data access information 362. The work procedure identification information 361 defines identification information that identifies the work procedure manual for the target business. The work procedure data access information 362 includes a memory address that serves as a key for accessing work procedure manual data stored in the transaction data storage unit 4 or the like that is managed outside the information collection and display system 1.

In order to reduce the time required to access the transaction data storage unit 4 that is managed outside the information collection and display system 1, it may be possible to hold information that is frequently used in the field data 100 in the definition information.

Next, an example of the connection information 314 constituting the business information 310 described in FIG. 3 will be described. The connection information 314 is information that defines the connection relationships between the business node 210, the part node 220, the worker node 230, the machine node 240, the finished product node 250, and the work procedure node 260 in the relationship data 200 described above. In other words, the data on the connection relationships of each node connected based on the connection information 314 matches the relationship data 200 described in FIG. 2, and is stored in the relationship data storage unit 15.

Figure 4 is a diagram illustrating an example of each node connected based on the connection information 314. In Figure 4, information such as the above-mentioned identification information and each data access information (see Figure 3) is omitted, and only information necessary for explaining the connection information 314 is described.

As shown in FIG. 4, the connection information 314 includes a label included in the business identification information 311, a manufacturing ID, a worker ID included in the connection information 314, a part ID_input, a part ID_output, a machine ID, a procedure ID, a previous process ID, and a next process ID.

In FIG. 4, all elements required to execute a task are defined as flowing from each of the 4M nodes (parts, workers, machines, work procedures) to the task node 210, and the output resulting from the execution of the task is defined as flowing from the task node 210 to the finished product node 250. The connection relationship between the nodes is defined by recording the IDs set for each node in the connection information 314. As a result, for example, the part ID (ID: Met001) set in the part node 220 is associated with the part ID (ID: Met001) of the task node 210, and a connection line is created from the part node 220 to the task node 210.

Similarly, for the other worker nodes 230, machine nodes 240, and work procedure nodes 260, the IDs set for each node match and are associated with the corresponding IDs for the business node 210, and a connection line is defined from each node to the business node 210. For the completed product node 250, the completed product ID set for the completed product node 250 is associated with the completed product ID for the business node 210, and a connection line is defined from the business node 210 to the completed product node 250.

Next, an example of the configuration of the site data 100 will be described. The site data 100 collected or generated by the data generating device 5 is acquired by the related data registration unit 11 according to the items and structure shown in the configuration shown in FIG. 5, which will be described below.

Figure 5 is a diagram illustrating an example of the configuration of the site data 100.

As shown in FIG. 5, the site data 100 includes business information 110, worker information 120, part information 130, finished product information 140, machine information 150, and work procedure information 160.

The business information 110 is composed of business identification information 111, occurrence date and time information 112, and actual value 113. The business identification information 111 is an identifier that defines the type of business for which the field data 100 was generated. The occurrence date and time information 112 is date and time information on when the field data 100 was generated, and for example, the date and time when the field data was collected or generated by the data generating device 5 is recorded. The actual value 113 is actual value information added by the data generating device 5, and for example, when the business is press processing, the number of finished products produced in one press operation and the number of good products sent to a subsequent process are recorded as actual value information.

The worker information 120 includes worker identification information 121, occurrence date and time information 122, and performance value 123. The worker identification information 121 is an identifier that identifies the worker who performed the work. For example, the worker identification information 121 that identifies the worker can be obtained by reading an ID card held by the worker before the work. The occurrence date and time information 122 is information on the date and time when the worker started the work. For example, when a worker performs pressing work, the date and time when one pressing work is started is recorded as the date and time information. The performance value 123 is information on the period during which the worker performed the work. For example, when a worker performs pressing work, the work period (time) required for one pressing work is recorded as the period information.

Part information 130 includes part identification information 131, generation date and time information 132, and performance value 133. Part identification information 131 is an identifier for identifying the material (part) used in the work. Generation date and time information 132 is information indicating the date and time when the part was produced. Performance value 133 is information such as the number of parts produced.

Finished product information 140 includes finished product identification information 141, occurrence date and time information 142, and actual value 143. Finished product identification information 141 is an identifier for identifying the finished product produced in the work. Occurrence date and time information 142 is information indicating the date and time when the finished product was produced. Actual value 143 is information such as the number of finished products produced in the work.

Machine information 150 includes machine identification information 151, occurrence date and time information 152, and performance value 153. Machine identification information 151 is an identifier for identifying the machine used in the work, and for example, this identifies the type of machine, such as a press machine, a paint machine, or an assembly machine. Occurrence date and time information 152 is information indicating the date and time when the machine started the work. Performance value 153 is information such as the operating time of the machine.

Work procedure information 160 is composed of work procedure identification information 161, occurrence date and time information 162, and actual value 163. Work procedure identification information 161 is an identifier for identifying the work procedure manual required for the work. Occurrence date and time information 162 is information indicating the date and time when the work procedure manual was acquired. Actual value 163 is information indicating the time required to perform the work according to the work procedure.

For example, multiple operations are connected in a straight line, and information on parts, machines, etc. is associated with each operation. Information associated with a specific operation may also be associated with multiple other operations. Similarly, information related to each of the multiple operations may be associated with a specific operation. Furthermore, when multiple operations exist in a manufacturing process, it is also possible that the operations are not directly connected to each other through the relationship information 500 described above, but are associated with other operations via information associated with each operation.

Figure 6 is a schematic diagram that explains only the connection relationships between each piece of information (nodes) in the association data 200. In Figure 6, first, the business A node uses materials as input to generate a part A node. After that, the business B node generates a part B node using the part A node generated by the business A node. The part B node associated as a finished product for the business B node is used as input for multiple other business nodes (business C node and business E node), and the part D node generated by the business D node and the part F node generated by the business F node are used as input for the business G node.

As shown in FIG. 6, multiple parts B are produced by performing task B, which corresponds to the previous process, and these multiple parts B are used as materials for tasks C and E, which correspond to the next process. It can also be seen that in task G, part D produced in task D and part F produced in task F are used as materials to produce the final finished product. In this case, in the operation data access information 313 constituting the operation information 310 shown in FIG. 3 described above, multiple parts B may be managed in lots in the actual operation information (i.e., multiple parts B are assigned one identifier), or one part B may be managed individually (i.e., one identifier is assigned to one part B). For example, when part B is managed in lots, part B is used in lots in tasks D and E, and when it is managed individually, part B is used in individual tasks D and E.

The connection relationships of each piece of information constituting the association data 200 are not limited to the example shown in FIG. 6. For example, multiple information nodes may be associated with one business node. Also, the connection relationships may be such that a part produced in a specific business is passed through another business and then returned to the specific business to be used again.

[Data Extraction Tool]
Next, a description will be given of a data extraction tool displayed on the user interface 7 such as a display. Using the data extraction tool displayed on the user interface 7, the user can easily extract the relationship data 200 by visually checking the data.

Figure 7 is a diagram illustrating an example of a data extraction tool displayed on a user interface 7 such as a display. In Figure 7, each node and connection line of the relational data registered in the relational data storage unit 15 is represented by circles and lines.

An input form is provided at the top of the data extraction tool screen, and the required search range for the association data 200 is entered into this input form. Multiple business nodes that fall within the entered search range are displayed at the bottom of the data extraction tool screen. In this business node, the association data 200 constructed with circles and lines can display the data items registered in the node. If this data item is a machine node, the actual value name of the machine node is displayed and obtained from the master data storage unit 3. The actual value name obtained from the master data storage unit 3 is converted by the dictionary database 6 from the name managed by the machine targeted by the machine node to a name that is easily recognizable by the user. Among the data items, the part displayed in a thick frame is the data specified by the user and data related to the specified data.

For example, when a data extraction tool is used to accept inputs such as the search start task, search end task, start date and time, and end date and time to specify the range from which the relationship data 200 is to be extracted, the relationship data search unit 12 of the information collection and display system 1 displays multiple business nodes in the manufacturing process to be extracted from the relationship data 200 extracted based on the specified range.

The user selects the node that the user wishes to search from the displayed business nodes (for example, the machine 1 node), and the relationship data search unit 12 displays a list of performance value names linked to the node selected by the user. In FIG. 7, based on the user's selection of the machine 1 node, the relationship data search unit 12 displays a list of performance value names for items 7 to 11 related to the machine 1 node.

Next, when the user selects one item from the list of performance values (e.g., item 9), the association data search unit 12 searches for other items such as item 1 of part A of task 1 that are associated with item 9 selected by the user, and displays them in a thick frame or the like, thereby enabling the extraction of items 1 related to item 9. Figure 7 shows the state in which three search results have been extracted for each of item 1, item 9, and item 13.

[Relationship data extraction process]
Next, the process of extracting relational data in the relational data search section 12 will be described.

Figure 8 is a flowchart of the process of extracting the relevance data 200 in the relevance data search unit 12. The process of extracting the relevance data 200 by the relevance data search unit 12 is performed using a data extraction tool (see Figure 7) displayed on a user interface 7 such as a display.

First, the data extraction tool displayed on the user interface 7 is used to input a period, scope of work, etc., into an input form to specify the range from which the relationship data 200 is to be extracted (step S101). The relationship data search unit 12 displays multiple business nodes in the manufacturing process to be extracted from the relationship data 200 extracted based on the specified range (step S102). The user selects the node that the user wishes to search from among the displayed business nodes (for example, the machine 1 node shown in FIG. 7) (step S103), and the relationship data search unit 12 displays a list of performance value names linked to the node selected by the user (step S104). In the embodiment, based on the user's selection of the machine 1 node, the relationship data search unit 12 displays a list of performance value names of items 12 to 15 related to the part C node.

Next, the user selects one from the list of performance values (for example, item 9 shown in FIG. 7) (step S105), and a sample of the extracted relationship data is displayed on a data display sub-screen (not shown) under the data extraction tool screen (step S106). The user repeats the processes of steps S101 to S106 multiple times until a list of data items that are necessary for data analysis and that the user judges to be related is extracted (step S107: No). When the selection is completed (step S107: Yes) and processing is executed, the relationship data search unit 12 obtains actual data from the transaction data storage unit 4 and master data storage unit 3 based on the operational data access information held by each node, and creates a data mart in the temporary storage unit 18 (step S108).

Next, a specific example of creating a data mart will be described. Figure 9 shows the processing procedure for tracing tasks according to their execution order. Here, tracing refers to the process of identifying the execution history of multiple tasks for a given item by determining the continuity of tasks using the identification information of the output item in the previous task and the identification information of the input item in the subsequent task. That is, in the processing procedure of Figure 9, the search starts from the upstream task, and tasks are traced downstream. Such tracing according to the execution order of tasks is used, for example, to identify parts or products that may have been affected from materials or parts where a problem occurred.

In the processing procedure of FIG. 9, the relevance data search section 12 executes the processes of the following steps S201 to S210.
Step S201
This is the start step of the process. In this step, the relationship data search unit 12 selects the relationship information of the task that is the starting point of the search. Then, the process proceeds to step S202.
Steps S202 to S208
This is a loop process whose end condition is "process all part nodes in the relevance information." The relevance data search unit 12 repeats steps S203 to S207 until the end condition is satisfied. When the end condition is satisfied, the process proceeds to step S209.

Step S203
The relationship data search unit 12 selects a part node in the relationship information, and the process proceeds to step S204.
Steps S204 to S207
This is a loop process whose end condition is "process all relevance information for the next task." The relevance data search unit 12 repeats steps S205 and S206 until the end condition is satisfied. When the end condition is satisfied, the process proceeds to step S208.

Step S205
The relevance data search unit 12 judges whether or not there is a part node having the same part ID as the selected part node in the relevance information of the next task. If there is a part node having the same part ID as the selected part node (step S205; Y), the process proceeds to step S206. If there is no part node having the same part ID as the selected part node (step S205; N), the process proceeds to step S207.
Step S206
The relationship data search unit 12 links the two pieces of relationship information using the part ID, and then proceeds to step S207.

Step S209
The relevance data search unit 12 judges whether the relevance information of all the tasks in the search range has been processed. If the relevance information of all the tasks in the search range has been processed (step S209; Y), the process ends. If the relevance information of all the tasks in the search range has not been processed (step S209; N), the process proceeds to step S210.
Step S210
The relevance data search unit 12 selects the relevance information of the task immediately following the selected task, and proceeds to step S202.

Figure 10 shows the processing procedure for tracing back the execution order of tasks. That is, in the processing procedure of Figure 10, the search starts from a downstream task, and tasks are traced upstream. Such tracing back the execution order of tasks is used, for example, to identify parts or materials that may have caused a problem from a product or part where a problem occurred.

Steps S301 to S310 in FIG. 10 correspond to steps S201 to S210 in FIG. 9. Specifically, steps S305, S307, and S310 target the "previous task" rather than the "next (following) task," which is different from the processing procedure in FIG. 9. The rest is the same, so a description will be omitted.

[Merge and split]
Next, the merging and branching of items will be explained. Merging refers to combining multiple input items that have been given different identification information so that they are indistinguishable and used in subsequent operations. Separation refers to separating multiple output items to use in subsequent operations. Here, input items are items such as materials and parts used as input for operations. Output items are items such as finished products and parts that are generated as output from operations. Merging and branching occur, for example, with items that are managed by lot.

Figure 11 is an explanatory diagram of the merging and branching of items. In Figure 11, an item with part ID "XXX" arrives and is stored, for example, on a specific shelf. At the time of arrival, this part is managed by order number, and the 50 pieces with order number "123" and the 100 pieces with order number "125" are identifiable. However, when they are stored on the same shelf, a merging occurs and it becomes impossible to identify which order number the 150 parts belong to.

In addition, in FIG. 11, this part will be used in a different assembly process after being released from the warehouse. Specifically, of the 150 parts, 70 are used in assembly 1 and 80 are used in assembly 2, resulting in a branch. When a branch occurs like this, if the same part ID is used, it is not possible to distinguish between the parts used in assembly 1 and the parts used in assembly 2.

Therefore, the information collection and display system 1 replaces the identification information when a merger or branching of items occurs. FIG. 12 is a specific example of a setting screen related to a merger or branching. The information collection and display system 1 accepts input of the business name, type, and ID conversion rules using the setting screen shown in FIG. 12. The business name is a name that identifies the business where a merger or branching occurs. The type indicates whether the ID conversion rules are to be set for a merger or a branching. The ID conversion rules indicate how the identification information is to be replaced.

Next, the processing procedure of the information collection and display system 1 when a merge or branch occurs will be described. Figure 13 shows the processing procedure when tracking according to the execution order of tasks, and includes conversion of IDs related to merges and branching.

In the processing procedure of FIG. 13, the relevance data search unit 12 of the information collecting and display system 1 executes the processes of the following steps S401 to S412.
Step S401
This is the start step of the process. In this step, the relevance data search unit 12 selects the relevance information of the task that is the starting point of the search. Then, the process proceeds to step S402.
Steps S402 to S410
This is a loop process whose end condition is "process all part nodes in the relevance information." The relevance data search unit 12 repeats steps S403 to S409 until the end condition is satisfied. If the end condition is satisfied, the process proceeds to step S411.

Step S403
The relevance data search unit 12 selects a part node in the relevance information, and the process proceeds to step S404.
Steps S404 to S409
This is a loop process whose end condition is "process all relevance information for the next task." The relevance data search unit 12 repeats steps S405 to S408 until the end condition is satisfied. When the end condition is satisfied, the process proceeds to step S410.

Step S405
The relationship data search unit 12 judges whether the job being processed is a target for lot branching or merging. If it is a target for lot branching or merging (step S405; Y), the process proceeds to step S406. If it is not a target for lot branching or merging (step S405; N), the process proceeds to step S407.
Step S406
The relationship data search unit 12 converts the part ID, and then the process proceeds to step S407.

Step S407
The relevance data search unit 12 judges whether or not there is a part node having the same part ID as the selected part node in the relevance information of the next task. If there is a part node having the same part ID as the selected part node (step S407; Y), the process proceeds to step S408. If there is no part node having the same part ID as the selected part node (step S407; N), the process proceeds to step S409.
Step S408
The relationship data search unit 12 links the two pieces of relationship information using the part ID, and then proceeds to step S409.

Step S411
The relevance data search unit 12 judges whether the relevance information of all the tasks in the search range has been processed. If the relevance information of all the tasks in the search range has been processed (step S411; Y), the process ends. If the relevance information of all the tasks in the search range has not been processed (step S411; N), the process proceeds to step S412.
Step S412
The relevance data search unit 12 selects the relevance information of the task immediately following the selected task, and proceeds to step S402.

Figure 14 shows the processing procedure when tracing back the execution order of tasks, and includes the conversion of IDs related to merging and branching. Steps S501 to S512 in Figure 14 correspond to steps S401 to S412 in Figure 13. Specifically, steps S507, S509, and S512 are directed at the "previous task" rather than the "next task", which is different from the processing procedure in Figure 13; otherwise, they are the same, so explanations will be omitted.

[Usage Probability]
Next, the probability of using an item related to a junction or branching will be described. When a junction or branching occurs, the information collection and display system 1 can associate a probability of use with the identification information of the input item or output item in the implementation history.

For example, when a merger occurs, the information collection and display system 1 calculates the usage probability of the merged input item from the number of merged input items and the number of input items after the merger, and the ratio. Similarly, when a branch occurs, the usage probability of the branched output item is calculated from the number of branched output items and the number of output items before the branch, and the ratio.

By using this usage probability, when a defect is found in a part, the information collection and display system 1 can show in which product and with what probability the part was used. Similarly, when an abnormality is found in a product, the information collection and display system 1 can show which part was used with what probability, and assist in identifying the part that may have caused the problem.

When displaying this usage probability, input items and output items whose usage probability does not meet a predetermined threshold may be excluded from the display. In addition, the target period for merging and branching may be limited.

For example, the information collection and display system 1 limits the target period to exclude mergers and divergences that occur after the subsequent tasks. This is because mergers and divergences that occur after the subsequent tasks do not affect the subsequent tasks.

In addition, the information collection and display system 1 limits the target period to exclude any merges or branchings that occurred before the specified date and time. Items that merged in the past may remain included in the items being shipped unless a situation occurs in which all of them are shipped out. However, because continuing to consider the influence of items that merged in the past would increase the processing load, the period before the specified date and time is excluded from the target period. Note that the specified date and time includes cases in which the date and time itself is directly specified, as well as cases in which a range is specified based on the current date and time, a range is specified based on the date and time of entry, and a range is specified based on the date and time of departure.

Figure 15 is a diagram explaining an example of a data extraction tool when using a usage probability. The data extraction tool shown in Figure 15 differs from Figure 7 in that it can accept input of the lot processing start date and time, the lot processing end date and time, and the lot probability range. It also differs from Figure 7 in that it further displays the lot probability in the search results. Here, lot processing is a merging or branching process. Also, lot probability is the usage probability of a part that has undergone lot processing.

Figure 16 is a concrete example of a merging range. As shown in Figure 16, when a lot processing start time and a lot processing start time are specified, analysis such as tracking is performed on parts A through C that merged during that time.

Similarly, Figure 17 shows a specific example of a branching range. As shown in Figure 17, when a lot processing start time and a lot processing start time are specified, analysis such as tracing is performed on parts X to Z that branched during that time.

Figure 18 is a flowchart showing the processing procedure for calculating the usage probability. The relationship data search unit 12 of the information collection and display system 1 first extracts the lot processing start time and lot processing end time (step S601). The relationship data search unit 12 then calculates the total number of parts used in lot merging or branching tasks within the lot processing time, i.e., within the target period (step S602). For each lot within the lot processing time, the relationship data search unit 12 calculates the lot probability as the ratio of the number of parts in each lot to the total number of parts (step S603), and ends the process.

If both a merge and a branch occur, the information collection and display system 1 can obtain the product of the probability of a merge and the probability of a branch as the usage probability of the corresponding item. FIG. 19 is an explanatory diagram of a case where a merge and a branch occur. Even in this case, when a lot processing start time and a lot processing start time are specified, analysis such as tracking will be performed on parts A to C that merged during that time and parts X to Z that branched during that time.

Figure 20 is a specific example of usage probability. As shown in Figure 20, the lot probability, or usage probability, at the merging of lot A is 22%. Similarly, the lot probability at the merging of lot B is 33%, and the lot probability at the merging of lot C is 45%. In addition, the lot probability at the branching of lot X is 11%, the lot probability at the branching of lot Y is 67%, and the lot probability at the branching of lot Z is 22%.

Figure 21 shows a specific example of the usage probability for a combination of merges and branches. As shown in Figure 21, the probability that the merge is lot A and the branch is lot X is 2.4%. Similarly, the probability that the merge is lot A and the branch is lot Y is 14.7%, and the probability that the merge is lot A and the branch is lot Z is 4.8%. Similarly, the probability that the merge is lot B and the branch is lot X is 3.6%, the probability that the merge is lot B and the branch is lot Y is 22.1%, and the probability that the merge is lot B and the branch is lot Z is 7.3%.

Figure 22 shows the processing procedure for tracking the order in which tasks are performed, and includes processing related to the probability of merging and branching.

In the processing procedure of FIG. 22, the relevance data search unit 12 of the information collecting and display system 1 executes the processing of the following steps S701 to S716.
Step S701
This is the start step of the process. In this step, the relationship data search unit 12 selects the relationship information of the task that is the starting point of the search. Then, the process proceeds to step S702.
Steps S702 to S711
This is a loop process whose end condition is "process all part nodes in the relevance information." The relevance data search unit 12 repeats steps S703 to S710 until the end condition is satisfied. If the end condition is satisfied, the process proceeds to step S712.

Step S703
The relevance data search unit 12 selects a part node in the relevance information, and the process proceeds to step S704.
Steps S704 to S710
This is a loop process whose end condition is "process all relevance information for the next task." The relevance data search unit 12 repeats steps S705 to S709 until the end condition is satisfied. If the end condition is satisfied, the process proceeds to step S711.

Step S705
The relationship data search unit 12 judges whether the job being processed is a target of lot branching or merging. If it is a target of lot branching or merging (step S705; Y), the process proceeds to step S706. If it is not a target of lot branching or merging (step S705; N), the process proceeds to step S408.
Step S706
The relationship data search unit 12 converts the part ID, and then the process proceeds to step S707.
Step S707
The relationship data search unit 12 calculates the lot probability corresponding to the part ID, and then proceeds to step S708.

Step S708
The relevance data search unit 12 judges whether or not there is a part node having the same part ID as the selected part node in the relevance information of the next task. If there is a part node having the same part ID as the selected part node (step S708; Y), the process proceeds to step S709. If there is no part node having the same part ID as the selected part node (step S708; N), the process proceeds to step S710.
Step S709
The relationship data search unit 12 links the two pieces of relationship information using the part ID, and then proceeds to step S710.

Step S712
The relevance data search unit 12 calculates the lot probability taking into consideration the lot combination, and proceeds to step S713.
Step S713
The relationship data search unit 12 judges whether the lot probability is within a specified range. If it is within the specified range (step S713; Y), the process proceeds to step S715. If it is not within the specified range (step S713; N), the process proceeds to step S714.
Step S714
The relevance data search section 12 excludes the search results from the display, and then the process proceeds to step S715.

Step S715
The relevance data search unit 12 judges whether the relevance information of all the tasks in the search range has been processed. If the relevance information of all the tasks in the search range has been processed (step S715; Y), the process ends. If the relevance information of all the tasks in the search range has not been processed (step S715; N), the process proceeds to step S716.
Step S716
The relevance data search unit 12 selects the relevance information of the task immediately following the selected task, and proceeds to step S702.

Figure 23 shows the processing procedure when tracing back the execution order of tasks, and includes processing related to the probability of merging and branching. Steps S801 to S816 in Figure 23 correspond to steps S701 to S716 in Figure 22. Specifically, steps S808, S810, and S816 are directed at the "previous task" rather than the "next task", which is different from the processing procedure in Figure 22; otherwise, they are the same, so explanations will be omitted.

As described above, the disclosed information collection system is a system that includes a data storage device that accumulates on-site data that is set or generated each time multiple tasks included in a process are performed, and an information system that processes the on-site data accumulated in the data storage device, wherein the on-site data includes identification information of an input item accepted as a target for processing in the corresponding task and identification information of an output item output as a result of the processing in the task, and the information system determines the continuity of tasks using the identification information of the output item in the previous task and the identification information of the input item in the subsequent task, thereby making it possible to track the execution history of the multiple tasks for a specified item, and the information system replaces the identification information when a merging of input items occurs, which combines multiple input items that have been given different identification information into an indistinguishable state, and/or a branching of output items occurs, which separates multiple output items and handles them individually.
With this configuration and operation, the information collection system can trace the execution history of a task that uses multiple parts to which the same management information is attached.

Furthermore, the above-mentioned information system associates a usage probability with the identification information of the input item and/or the output item in the performance history when the junction and/or the branch occurs.
As an example, when the merging occurs, the information system can calculate the usage probability of the merged input item from the number of merged input items and the number and ratio of input items after the merging.
Furthermore, when the branch occurs, the information system can determine the usage probability of the branched output item from the ratio between the number of branched output items and the number of output items before the branch.
Furthermore, when the merging and branching occur, the information system can obtain the product of the probability of the merging and the probability of the branching as the usage probability of the corresponding item.
In this manner, the above-mentioned information system is capable of evaluating and displaying the possibility that an item related to merging or branching has been used.

Furthermore, the above-mentioned information system can exclude from displaying input items and/or output items whose usage probability is less than a predetermined threshold value.
Furthermore, the above-mentioned information system can limit a target period for the merging and/or branching.
Specifically, the information system limits the target period to exclude mergers and/or divergences that occurred after the subsequent task, and also limits the target period to exclude mergers and/or divergences that occurred before a specified date and time.
In this way, by controlling the range of areas to be judged for merging or branching and the range of display, effective processing and output are possible.

The present invention is not limited to the above-described embodiment, but includes various modified examples. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to having all of the configurations described. Furthermore, the configurations may be replaced or added, rather than being deleted.

For example, the information system may be configured to take into account branching (without considering merging) when tracing the order in which multiple tasks are performed, and to take into account merging (without considering branching) when tracing back the order in which multiple tasks are performed.

1: Information collection and display system, 3: Master data storage unit, 4: Transaction data storage unit, 5: Data generation device, 6: Dictionary database, 7: User interface, 8: Application, 10: Relevance data model creation unit, 11: Relevance data registration unit, 12: Relevance data search unit, 13: Stored data acquisition unit, 14: Analysis data storage unit, 15: Relevance data storage unit, 16: Data provision API unit, 17: Comparison data definition unit, 18: Temporary storage unit, 200: Relevance data, 210: Business node, 220: Part node, 230: Worker node, 240: Machine node, 250: Finished product node, 260: Work procedure node, 300: Definition information, 310: Business information, 320: Worker information, 330: Part information, 340: Finished product information, 350: Machine information, 360: Work procedure information

Claims (12)

A system including: a data storage device that stores on-site data that is set or generated each time a plurality of tasks included in a process are performed; and an information system that processes the on-site data stored in the data storage device,
The on-site data includes identification information of an input object accepted as a target for processing in a corresponding task, identification information of an output object output as a result of the processing in the task , information of a worker involved in the task, information of a work procedure for the task, and information of a machine involved in the task ,
the information system determines a connection relationship of nodes including a task node indicating the task, a worker node indicating the worker, a work procedure node indicating the work procedure, a machine node indicating the machine, and a part node indicating the input and output,
The information system displays a connection relationship of the nodes, accepts a selection of a node, and acquires the on-site data for the selected node.
The information system determines the continuity of the tasks using identification information of an output object in a previous task and identification information of an input object in a subsequent task based on the acquired on-site data , thereby making it possible to trace the execution history of the multiple tasks for a specified input object or output object;
The information collection system is characterized in that it replaces identification information when a merging of input items occurs, which combines multiple input items that have been given different identification information into an indistinguishable state, and/or a branching of output items occurs, which separates multiple output items and handles them individually. The information collection system according to claim 1, characterized in that the information system associates a usage probability with the identification information of the input and/or output in the execution history when the merging and/or branching occurs. The information collection system according to claim 2, characterized in that, when the merger occurs, the information system calculates the usage probability of the merged input items from the number of merged input items and the number of input items after the merger and the ratio. The information collection system according to claim 2, characterized in that, when the branch occurs, the information system calculates the usage probability of the branched output from the ratio between the number of branched outputs and the number of outputs before the branch. The information collection system according to claim 2, characterized in that, when the merging and branching occur, the information system calculates the product of the probability of the merging and the probability of the branching as the usage probability of the corresponding input or output. The information collection system according to claim 2, characterized in that the information system excludes from display any input and/or output object whose usage probability is less than a predetermined threshold. The information collection system according to claim 1, characterized in that the information system limits the target period for the merging and/or branching. The information collection system according to claim 7, characterized in that the information system limits the target period to exclude mergers and/or divergences that occur after the subsequent task. The information collection system according to claim 7, characterized in that the information system limits the target period to exclude mergers and/or divergences that occurred before a specified date and time. The information collection system according to claim 1, characterized in that when the information system performs the tracing in accordance with the execution order of the plurality of tasks, the tracing takes into account the branching. The information collection system according to claim 1, characterized in that when tracing back the execution order of the multiple tasks, the information system performs tracing taking into account the merging. 1. An information collection method in a system including a data storage device that stores on-site data that is set or generated each time a plurality of tasks included in a process are performed, and an information system that processes the on-site data stored in the data storage device, comprising:
The data storage device stores the on-site data including identification information of an input object accepted as a target for processing in a corresponding task, identification information of an output object output as a result of the processing in the task , information of a worker involved in the task, information of a work procedure for the task, and information of a machine involved in the task ;
a step in which the information system determines a connection relationship of nodes including a task node indicating the task, a worker node indicating the worker, a work procedure node indicating the work procedure, a machine node indicating the machine, and part nodes indicating the input and output;
the information system displays a connection relationship of the nodes, accepts a selection of a node, and acquires the on-site data for the selected node;
The information system determines the continuity of the tasks using identification information of an output object in a previous task and identification information of an input object in a subsequent task based on the acquired on-site data , thereby tracking the execution history of the multiple tasks for a specified input object or output object;
Including,
The information gathering method according to the present invention further comprises a step of replacing identification information when a merging of input items occurs, whereby multiple input items that have been given different identification information are combined into an indistinguishable item, and/or a branching of output items occurs, whereby multiple output items are separated and handled individually.

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